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Dimension Reduction of Multidimensional Structured and Unstructured Datasets through Ensemble Learning of Neural Embeddings

  • Identification data

    Identifier: imarina:9378965
    Authors:
    Alvarado-Pérez JCGarcia MAPuig D
    Abstract:
    Dimension reduction aims to project a high-dimensional dataset into a low-dimensional space. It tries to preserve the topological relationships among the original data points and/or induce clusters. NetDRm, an online dimensionality reduction method based on neural ensemble learning that integrates different dimension reduction methods in a synergistic way, is introduced. NetDRm is designed for datasets of multidimensional points that can be either structured (e.g., images) or unstructured (e.g., point clouds, tabular data). It starts by training a collection of deep residual encoders that learn the embeddings induced by multiple dimension reduction methods applied to the input dataset. Subsequently, a dense neural network integrates the generated encoders by emphasizing topological preservation or cluster induction. Experiments conducted on widely used multidimensional datasets (point-cloud manifolds, image datasets, tabular record datasets) show that the proposed method yields better results in terms of topological preservation ((Formula presented.) curves), cluster induction (V measure), and classification accuracy than the most relevant dimension reduction methods.
  • Others:

    Author, as appears in the article.: Alvarado-Pérez JC; Garcia MA; Puig D
    Department: Enginyeria Informàtica i Matemàtiques
    URV's Author/s: Puig Valls, Domènec Savi
    Keywords: Cluster inductions Dimensionality reductions Ensemble learning Manifold approximations Online processing Topological preservations Unsupervised deep networks
    Abstract: Dimension reduction aims to project a high-dimensional dataset into a low-dimensional space. It tries to preserve the topological relationships among the original data points and/or induce clusters. NetDRm, an online dimensionality reduction method based on neural ensemble learning that integrates different dimension reduction methods in a synergistic way, is introduced. NetDRm is designed for datasets of multidimensional points that can be either structured (e.g., images) or unstructured (e.g., point clouds, tabular data). It starts by training a collection of deep residual encoders that learn the embeddings induced by multiple dimension reduction methods applied to the input dataset. Subsequently, a dense neural network integrates the generated encoders by emphasizing topological preservation or cluster induction. Experiments conducted on widely used multidimensional datasets (point-cloud manifolds, image datasets, tabular record datasets) show that the proposed method yields better results in terms of topological preservation ((Formula presented.) curves), cluster induction (V measure), and classification accuracy than the most relevant dimension reduction methods.
    Thematic Areas: Artificial intelligence Automation & control systems Computer science, artificial intelligence Computer vision and pattern recognition Control and systems engineering Electrical and electronic engineering Human-computer interaction Materials science (miscellaneous) Mechanical engineering Robotics
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: domenec.puig@urv.cat
    Author identifier: 0000-0002-0562-4205
    Record's date: 2024-10-12
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://onlinelibrary.wiley.com/doi/10.1002/aisy.202400178
    Papper original source: Advanced Intelligent Systems.
    APA: Alvarado-Pérez JC; Garcia MA; Puig D (2024). Dimension Reduction of Multidimensional Structured and Unstructured Datasets through Ensemble Learning of Neural Embeddings. Advanced Intelligent Systems, (), -. DOI: 10.1002/aisy.202400178
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Article's DOI: 10.1002/aisy.202400178
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2024
    Publication Type: Journal Publications
  • Keywords:

    Artificial Intelligence,Automation & Control Systems,Computer Science, Artificial Intelligence,Computer Vision and Pattern Recognition,Control and Systems Engineering,Electrical and Electronic Engineering,Human-Computer Interaction,Materials Science (Miscellaneous),Mechanical Engineering,Robotics
    Cluster inductions
    Dimensionality reductions
    Ensemble learning
    Manifold approximations
    Online processing
    Topological preservations
    Unsupervised deep networks
    Artificial intelligence
    Automation & control systems
    Computer science, artificial intelligence
    Computer vision and pattern recognition
    Control and systems engineering
    Electrical and electronic engineering
    Human-computer interaction
    Materials science (miscellaneous)
    Mechanical engineering
    Robotics
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